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A Weights Direct Determination Neural Network for International Standard Classification of Occupations

Author

Listed:
  • Dimitris Lagios

    (Data and Media Laboratory, Department of Electrical and Computer Engineering, University of Peloponnese, 26334 Patras, Greece)

  • Spyridon D. Mourtas

    (Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece
    Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems”, Siberian Federal University, Prosp. Svobodny 79, 660041 Krasnoyarsk, Russia)

  • Panagiotis Zervas

    (Data and Media Laboratory, Department of Electrical and Computer Engineering, University of Peloponnese, 26334 Patras, Greece)

  • Giannis Tzimas

    (Data and Media Laboratory, Department of Electrical and Computer Engineering, University of Peloponnese, 26334 Patras, Greece)

Abstract

Multiclass classification is one of the most popular machine learning tasks. The main focus of this paper is to classify occupations according to the International Standard Classification of Occupations (ISCO) using a weights and structure determination (WASD)-based neural network. In general, WASD-trained neural networks are known to overcome the drawbacks of conventional back-propagation trained neural networks, such as slow training speed and local minimum. However, WASD-based neural networks have not yet been applied to address the challenges of multiclass classification. As a result, a novel WASD for multiclass classification (WASDMC)-based neural network is introduced in this paper. When applied to two publicly accessible ISCO datasets, the WASDMC-based neural network displayed superior performance across all measures, compared to some of the best-performing classification models that the MATLAB classification learner app has to offer.

Suggested Citation

  • Dimitris Lagios & Spyridon D. Mourtas & Panagiotis Zervas & Giannis Tzimas, 2023. "A Weights Direct Determination Neural Network for International Standard Classification of Occupations," Mathematics, MDPI, vol. 11(3), pages 1-14, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:629-:d:1047474
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    References listed on IDEAS

    as
    1. Heinesen, Eskil & Imai, Susumu & Maruyama, Shiko, 2018. "Employment, job skills and occupational mobility of cancer survivors," Journal of Health Economics, Elsevier, vol. 58(C), pages 151-175.
    2. Simos, Theodore E. & Katsikis, Vasilios N. & Mourtas, Spyridon D., 2022. "Multi-input bio-inspired weights and structure determination neuronet with applications in European Central Bank publications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 451-465.
    3. Spyridon D. Mourtas, 2022. "A weights direct determination neuronet for time‐series with applications in the industrial indices of the Federal Reserve Bank of St. Louis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1512-1524, November.
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